Automated Robot Skill Learning from Demonstration for Various Robot Systems

Publication

Lisa Gutzeit, Alexander Fabisch, Christoph Petzold, Hendrik Wiese, Frank Kirchner

In: KI 2019: Advances in Artificial Intelligence. German Conference on Artificial Intelligence (KI-2019) September 23-26 Kassel Germany Pages 168-181 LNAI Springer 2019.

Abstract

Transferring human movements to robotic systems is of high interest to equip the systems with new behaviors without expert knowledge. Typ- ically, skills are often only learned for a very specific setup and a certain robot. We propose a modular framework to learn skills that is applica- ble on different robotic systems without adaptations. Our work builds on the recently introduced BesMan Learning Platform, which comprises the full workflow to transfer human demonstrations to a system, includ- ing automatized behavior segmentation, imitation learning, reinforcement learning for motion refinement, and methods to generalize to related tasks. For this paper, we extend this approach in order that different skills can be imitated by various systems in an automated fashion with a minimal amount of configuration, e.g., definition of the target system and environ- ment. For this, we focus on the imitation of the demonstrated movements and show their transferability without movement refinement. We demon- strate the generality of the approach on a large dataset, consisting of about 700 throwing demonstrations. Nearly all of these human demonstrations are successfully transferred to four different robot target systems, namely Universal Robot’s UR5 and UR10, KUKA LBR iiwa, and DFKI’s robot COMPI. An analysis of the quality of the imitated movement on the real UR5 robot shows that useful throws can be executed on the system which can be used as starting points for further movement refinement.

Projekte

COROMA - Cognitively enhanced robot for flexible manufacturing of metal and composite parts,
TransFit - Flexible interaction for infrastructures establishment by means of teleoperation and direct collaboration; transfer into industry 4.0

20190712_Automated_Robot_Skill_Learning_from_Demonstration_for_Various_Robot_Systems.pdf (pdf, 2 MB )

Automated Robot Skill Learning from Demonstration for Various Robot Systems

Abstract

Projekte

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